GitHub Copilot has evolved from a basic autocomplete engine into an intelligent AI assistant capable of understanding and navigating large-scale codebases. One of the most powerful capabilities it brings to developers is the ability to reason across multiple files in a project. This seemingly magical feature is not a trivial extension of autocomplete — it is the result of sophisticated orchestration involving context retrieval, symbol analysis, vector embeddings, token prioritization, and prompt construction under strict limitations.
This article presents a deeply technical examination of how GitHub Copilot internally handles multi-file context. The purpose is to demystify its architectural design, explain its data processing pipeline, and highlight the algorithms and data structures powering its context-aware capabilities.